1. Technical Field
The present invention relates to a method, computer system, and computer program product for enabling financial institutions and government regulatory agencies to analyze financial transaction data for detecting out-of-norm financial transactions, such as fraudulent financial transactions, that occur between entities such as individuals, corporations, and government agencies.
2. Related Art
Financial transactions facilitated by financial institutions such as banks, stock brokerage houses, insurance companies, etc. are recorded in databases of computer systems. The volume of such financial transactions that occur on a daily basis will typically be in the several hundred millions. Although a majority of these financial transactions are legitimate and constitute legal transactions, a small minority of such transactions may involve fraudulent activity such as money laundering, tax evasion, and “insider” stock trading.
In addition to processing the daily volume of financial transactions, a historical archive of this data has to be maintained by financial institutions as mandated by government regulations. A comprehensive analysis of transactions requires access to historical data that has to be collected from a multitude of financial institutions, since a majority of transactions typically involve separate financial institutions that are operating on behalf of parties involved in the transaction. Timely detection of fraudulent activities requires rapid analysis of this consolidated data that can contain billions of transaction records.
A fraudulent transaction typically involves more than just a pair of participants. Most fraudulent transactions routinely involve several “intermediaries” so that the fraudulent intents are well-concealed. Hence, an effective approach for fraud detection will require analysis of transactions spanning multiple participants operating through different financial institutions and in different geographical areas.
Unfortunately, the dynamic nature and the size of transaction data sets coupled with the need to analyze transactions involving multiple participants renders known techniques unsuitable for timely detection of fraudulent transactions.
Accordingly, there is a need for a method, computer system, and computer program product that can efficiently analyze financial transactions contained in very large data sets for rapid detection of fraudulent activities and identification of entities responsible for such transactions.